| Metric | Value |
|---|---|
| AIC | 670.97 |
| BIC | 689.06 |
| R2 | 0.83 |
| R2 (adj.) | 0.83 |
| RMSE | 6.91 |
| Sigma | 7.13 |
For interpretation of performance metrics, please refer to this documentation.
| Parameter | Coefficient | SE | 95% CI | t(92) | p |
|---|---|---|---|---|---|
| (Intercept) | 0.18 | 7.05 | (-13.81, 14.18) | 0.03 | 0.979 |
| education | 3.66 | 0.65 | (2.38, 4.95) | 5.67 | < .001 |
| income | 1.04e-03 | 2.62e-04 | (5.22e-04, 1.56e-03) | 3.98 | < .001 |
| women | 6.44e-03 | 0.03 | (-0.05, 0.07) | 0.21 | 0.832 |
| type (linear) | 4.18 | 2.78 | (-1.35, 9.71) | 1.50 | 0.137 |
| type (quadratic) | 4.79 | 1.54 | (1.72, 7.86) | 3.10 | 0.003 |
To find out more about table summary options, please refer to this documentation.
Error in match.arg(tolower(range), c("range", "iqr", "ci", "hdi", "eti", : 'arg' should be one of "range", "iqr", "ci", "hdi", "eti", "sd", "mad"
Error in lapply(text_modelbased, function(i) {: object 'text_modelbased' not found
Error in is.ggplot(x): object 'all_plots' not found
Error in eval(expr, envir, enclos): object 'text_modelbased' not found
We fitted a linear model (estimated using OLS) to predict prestige with education (formula: prestige ~ education + income + women + type). The model explains a statistically significant and substantial proportion of variance (R2 = 0.83, F(5, 92) = 93.07, p < .001, adj. R2 = 0.83). The model’s intercept, corresponding to education = 0, is at 0.18 (95% CI (-13.81, 14.18), t(92) = 0.03, p = 0.979). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear model (estimated using OLS) to predict prestige with income (formula: prestige ~ education + income + women + type). The model explains a statistically significant and substantial proportion of variance (R2 = 0.83, F(5, 92) = 93.07, p < .001, adj. R2 = 0.83). The model’s intercept, corresponding to income = 0, is at 0.18 (95% CI (-13.81, 14.18), t(92) = 0.03, p = 0.979). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation., We fitted a linear model (estimated using OLS) to predict prestige with women (formula: prestige ~ education + income + women + type). The model explains a statistically significant and substantial proportion of variance (R2 = 0.83, F(5, 92) = 93.07, p < .001, adj. R2 = 0.83). The model’s intercept, corresponding to women = 0, is at 0.18 (95% CI (-13.81, 14.18), t(92) = 0.03, p = 0.979). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation. and We fitted a linear model (estimated using OLS) to predict prestige with type (formula: prestige ~ education + income + women + type). The model explains a statistically significant and substantial proportion of variance (R2 = 0.83, F(5, 92) = 93.07, p < .001, adj. R2 = 0.83). The model’s intercept, corresponding to type = , is at 0.18 (95% CI (-13.81, 14.18), t(92) = 0.03, p = 0.979). Within this model:
Standardized parameters were obtained by fitting the model on a standardized version of the dataset. 95% Confidence Intervals (CIs) and p-values were computed using a Wald t-distribution approximation.
The model explains a statistically significant and substantial proportion of variance (R2 = 0.83, F(5, 92) = 93.07, p < .001, adj. R2 = 0.83)
---
title: "Regression model summary from `{easystats}`"
output:
flexdashboard::flex_dashboard:
theme:
version: 4
# bg: "#101010"
# fg: "#FDF7F7"
primary: "#0054AD"
base_font:
google: Prompt
code_font:
google: JetBrains Mono
params:
model: model
check_model_args: check_model_args
parameters_args: parameters_args
performance_args: performance_args
---
```{r setup, include=FALSE}
library(flexdashboard)
library(easystats)
# Since not all regression model are supported across all packages, make the
# dashboard chunks more fault-tolerant. E.g. a model might be supported in
# `{parameters}`, but not in `{report}`.
#
# For this reason, `error = TRUE`
knitr::opts_chunk$set(
error = TRUE,
out.width = "100%"
)
```
```{r}
# Get user-specified model data
model <- params$model
# Is it supported by `{easystats}`? Skip evaluation of the following chunks if not.
is_supported <- insight::is_model_supported(model)
if (!is_supported) {
unsupported_message <- sprintf(
"Unfortunately, objects of class '%s' are not yet supported in {easystats}.\n
For a list of supported models, see `insight::supported_models()`.",
class(model)[1]
)
}
```
Model fit
=====================================
Column {data-width=700}
-----------------------------------------------------------------------
### Assumption checks
```{r check-model, eval=is_supported, fig.height=10, fig.width=10}
check_model_args <- c(list(model), params$check_model_args)
do.call(performance::check_model, check_model_args)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=300}
-----------------------------------------------------------------------
### Indices of model fit
```{r, eval=is_supported}
# `{performance}`
performance_args <- c(list(model), params$performance_args)
table_performance <- do.call(performance::performance, performance_args)
print_md(table_performance, layout = "vertical", caption = NULL)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
For interpretation of performance metrics, please refer to <a href="https://easystats.github.io/performance/reference/model_performance.html" target="_blank">this documentation</a>.
Parameter estimates
=====================================
Column {data-width=550}
-----------------------------------------------------------------------
### Plot
```{r dot-whisker, eval=is_supported}
# `{parameters}`
parameters_args <- c(list(model), params$parameters_args)
table_parameters <- do.call(parameters::parameters, parameters_args)
plot(table_parameters)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=450}
-----------------------------------------------------------------------
### Tabular summary
```{r, eval=is_supported}
print_md(table_parameters, caption = NULL)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
To find out more about table summary options, please refer to <a href="https://easystats.github.io/parameters/reference/model_parameters.html" target="_blank">this documentation</a>.
Predicted Values
=====================================
Column {data-width=600}
-----------------------------------------------------------------------
### Plot
```{r expected-values, eval=is_supported, fig.height=10, fig.width=10}
# `{modelbased}`
int_terms <- find_interactions(model, component = "conditional", flatten = TRUE)
con_terms <- find_variables(model)$conditional
if (is.null(int_terms)) {
model_terms <- con_terms
} else {
model_terms <- clean_names(int_terms)
int_terms <- unique(unlist(strsplit(clean_names(int_terms), ":", fixed = TRUE)))
model_terms <- c(model_terms, setdiff(con_terms, int_terms))
}
text_modelbased <- lapply(unique(model_terms), function(i) {
grid <- get_datagrid(model, at = i, range = "grid", preserve_range = FALSE)
estimate_expectation(model, data = grid)
})
ggplot2::theme_set(theme_modern())
# all_plots <- lapply(text_modelbased, function(i) {
# out <- do.call(visualisation_recipe, c(list(i), modelbased_args))
# plot(out) + ggplot2::ggtitle("")
# })
all_plots <- lapply(text_modelbased, function(i) {
out <- visualisation_recipe(i, show_data = "none")
plot(out) + ggplot2::ggtitle("")
})
see::plots(all_plots, n_columns = round(sqrt(length(text_modelbased))))
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=400}
-----------------------------------------------------------------------
### Tabular summary
```{r, eval=is_supported, results="asis"}
for (i in text_modelbased) {
tmp <- print_md(i)
tmp <- gsub("Variable predicted", "\nVariable predicted", tmp)
tmp <- gsub("Predictors modulated", "\nPredictors modulated", tmp)
tmp <- gsub("Predictors controlled", "\nPredictors controlled", tmp)
print(tmp)
}
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Text reports
=====================================
Column {data-width=500}
-----------------------------------------------------------------------
### Textual summary
```{r, eval=is_supported, results='asis', collapse=TRUE}
# `{report}`
text_report <- report(model)
text_report_performance <- report_performance(model)
gsub("]", ")", gsub("[", "(", text_report, fixed = TRUE), fixed = TRUE)
cat("\n")
gsub("]", ")", gsub("[", "(", text_report_performance, fixed = TRUE), fixed = TRUE)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```
Column {data-width=500}
-----------------------------------------------------------------------
### Model information
```{r, eval=is_supported}
model_info_data <- insight::model_info(model)
model_info_data <- datawizard::data_to_long(as.data.frame(model_info_data))
DT::datatable(model_info_data)
```
```{r, eval=!is_supported}
cat(unsupported_message)
```